The physical surface of the earth is undergoing constant change: new cities are being built, existing cities are expanding, and lakes and other water bodies are changing their extent. In order to predict the effects of these changes and to develop policies to address them, it is important to have an accurate assessment of current land cover and how the land cover has changed over time.
One way to measure land cover is to classify areas based on aerial image data. Such image data includes spectral data for various frequencies such red, green, and blue visual light frequencies and infrared light frequencies. Using this data, a classifier attempts to classify each area into a single land cover class such as urban, vegetation and water. Changes in land cover are then detected by determining when the classification for an area changes between two images of the area. Such classifiers are trained based on labeled examples of each type of land cover.